System and method for forecasting probability of precipitation

ABSTRACT

A system and method are disclosed for forecasting probability of precipitation values and most probable precipitation amount values for, preferably, three hour time period increments starting from the present hour through approximately hour 96 (i.e., four days) or beyond. The values are recalculated at the beginning of each hour, based upon existing forecasting information and meteorological data. The values are communicated to end users through a communications channel such as the Internet.

BACKGROUND OF THE INVENTION

1. Field of Invention

The present invention relates to systems for forecasting andcommunicating meteorological information, and in particular, to systemsfor producing multi-period probability of precipitation (POP) forecastsand disseminating the same through communications channels including,but not limited to the Internet, wireless devices, and broadcastsystems.

2. Description of the Prior Art

The present invention was developed primarily in response to the needfor enhanced weather information based on probability forecasts, a needrecognized by members of The American Meteorological Society (AMS) in a2002 Statement.

According to the AMS, weather forecasts have improved dramatically overthe past two decades. In fact, forecasts produced by operationalforecasters using meteorological observation data and forecasts producedby numerical models have become more accurate for nearly all weatherelements and for most time and space scales of interest. Those forecastscontribute important information to decision makers and are valuable toa multitude of users, including the general public, the military,aircraft operators, businesses, and emergency managers, to name a few.

Progress has also been observed in developing accurate probabilityforecasts, which have a significant economic benefit because a sizableportion of the U.S. economy is weather sensitive. That progress has beenimportant because POP forecasts (the percentage chance that a measurableamount of precipitation (at least 0.01 inches of rain or ice or 0.1inches of snow) will fall at a specific location during a specific timeperiod) are well accepted by many end users, including the generalpublic.

Probability forecasts also have several benefits over categoricalforecasts. One benefit is that they contain more information: theuncertainty in the forecast is expressed as part of the forecast. Thus,the end user is made aware of the uncertainty in the forecast and canuse that additional information in making decisions. However, not allend users understand the information provided by probability forecastsor the meteorological event being represented by the forecasts. In itssimplest terms, most people understand what is meant by the probabilityof an event occurring, such as the probability of obtaining “heads” whenflipping a coin. What is significantly less intuitive to end users ofPOP forecasts is what is meant by a forecast that says “there is a 30%POP for State College, Pa., tomorrow.” Often, that forecast isinterpreted ostensibly as meaning that it will rain over 30% of theState College, Pa., area tomorrow or there is a 30% chance it will rainsomewhere in the immediate region tomorrow. Other combinations of thoseand other interpretations are also likely. Thus, while quantitativeprecipitation forecasts have become more accurate, have advantages overcategorical forecasts, are generally accepted by the general public, andare relatively simple in terms of the information provided, they are, bytheir very nature, rather complex.

To simplify the complexity, according to the AMS, a POP forecast shouldprovide a probability of any desired amount, say 0.5 to 1.0 inches ofprecipitation, for any desired time interval. Until the presentinvention, POP forecasts have been issued for periods of 24, 12 or sixhours, starting at a preset, fixed time, such as 7 am, and this timewould not change dynamically. Those intervals are too long to providesufficient detail as to when precipitation might occur and when it ismost likely to occur. Shorter intervals, such as three hours, would besignificantly better in terms of accuracy. The problem with shorterintervals, however, is that simply dividing the longer period intoshorter intervals can result in inaccurate or even meaninglessforecasts. For example, given a probability of precipitation for a6-hour period (POP-6) of 60%, simply dividing the POP-6 hours into twothree-hour intervals and calculating POP-3 values based on the data forthose hours may not accurately represent the probability of forecast forthe two intervals. In fact, as the time interval for the POP getsshorter, eventually it becomes physically impossible to have enoughprecipitation fall to qualify under the definition of a measurableamount. A more robust computational method is needed to generateaccurate probability of precipitation values for periods shorter than 6hours.

Another problem with current systems is that once the preset startingtime for a period has passed (i.e., the starting time for the POP forthe two 12-hour periods beginning at 7 am-7 pm), the POP time periodswould not move with the clock. That means that at, for example, 9 am,the POP forecast available would be for the 7 am-7 pm period (orpossibly the remainder of that period) and for 7 pm-7 am, rather than 9am-9 pm and 9 pm-9 am time periods. Further, once 0.01 inch ofprecipitation has fallen, the probability of precipitation would bydefinition be 100% for the period. Thus, a probability of precipitationthat does not reset every hour during precipitation would tell the usernothing about the future weather, and would therefore be of no value. Inaddition, a probability of precipitation that is less than 100% and doesnot reset during precipitation, would be inconsistent with the weatherthat has occurred and the definition of POP, and therefore inaccurateand confusing. Accordingly, probability of precipitation forecasts thatreset to the present time would also be better in terms of accuracy andusefulness.

U.S. Pat. No. 6,424,917, for example, discloses a system and method forspatial synoptic classification using “sliding seed days” as modelinputs rather than fixed time periods. That method is disclosed as beingadvantageous in terms of forecasting synoptic conditions.

To also simplify the complexity of probability forecasts, according tothe AMS, there should be new ways for displaying and communicating theprobabilistic information compared to those presently available. TheNational Weather Service (NWS), operating under the auspices of theNational Oceanic and Atmospheric Administration (NOAA), has recentlybeen experimenting with communicating probability forecast informationto various interested parties. Called a graphical forecast, the NWSchose a graphical (map) approach for displaying information on its website that may be accessed over the Internet. The information availablefor displaying is generated and maintained in NOAA's National DigitalForecast Database, and includes POP-12 data (the percentage chance thata measurable amount of precipitation (at least 0.01 inches of rain orice or 0.1 inches of snow) will fall at a specific location during a12-hour time period). However, even on the NWS's “Experimental Products”web site, they do not offer any POP forecasts for less than 6 hours nordo they offer any POP forecasts that are not for pre-set, non-rollingtime periods.

Commercial companies also use their web pages for providingweather-related information, but other communication channels have alsobeen used, such as electronic mail over the Internet and facsimile, asin the case of “E-Weather,” which is provided to users by SkyBit, Inc.The “E-Weather” data may be in the form of a table array (3-hour timeperiod increments along the top of the table and weather parameters(e.g., temperature, humidity, POP-6, etc.) along the left edge of thetable). It is significant to note that even in this table array,although most data is presented in 3-hour increments, the POP forecastsare presented in 6 hour increments. U.S. Pat. No. 6,654,689 disclosesmethods of providing meteorological data (storm warnings) that includeusing a server connected to the Internet (or another network) forproviding web-based text and images to a client's computer, uploadingthe same information directly to a third party's web site, sending thedata via a pager or phone, and communicating the data and otherinformation through broadcast systems (television or satellite). Asdiscussed in U.S. Pat. No. 6,498,987, advances in computer connectivitytechnology available in most locations have allowed advances incommunicating weather information to end users via the Internet by usingweb pages maintained on servers connected to the Internet and operatedby various communications companies, such as local television and radiocompanies.

Before any information may be communicated to an end user, however, theappropriate probability forecast information must be determined. Systemsand methods for generating the information are well known in the art.U.S. Patent Application Serial No. 2002/0114517A1, for example,discloses a short-term storm predictor system whereby meteorologicalimage data from satellites or other sources is computer-processed togenerate a 10- to 120-minute severe thunderstorm forecast (the raw datamay be available, for example, from NOAA's NEXRAD network of radarsystems). The results are communicated by a graphical representation ofthe event centered on the graph. Each pixel represents a location withina region and can be assessed a numerical value that represents, forexample, the rate of precipitation. U.S. Pat. No. 6,128,578 alsoanalyzes time-series changes in real-time radar images to forecastprecipitation, the output being a graphical display having contoured andcolored probability rings superimposed over a spatial region.

Although many features of the present invention are described in theprior art, none of the prior art patents are directed to a systemspecifically for providing location-specific, time-sliding (less thansix-hour) probability of precipitation forecasts via the Internet.Moreover, the prior art do not contain any suggestion or motivation tocalculate probability of precipitation values for time periods less thansix hours or on a rolling time basis. There remains, therefore, the needfor such a system to provide more accurate forecasts that may be used bydecision makers planning activities that are weather sensitive.

SUMMARY OF THE INVENTION

A POP forecast provides the percentage chance that a measurable amountof precipitation (at least 0.01 inches of rain or ice or 0.1 inches ofsnow) will fall at a specific location during a specific time period. A“Trace” of precipitation is defined as a finite, non-zero amount ofprecipitation that falls, that is less than a measurable amount. A lowPOP means that there is a small chance of measurable precipitation;conversely, a high POP means the chance is greater. Once the forecastperiod has occurred, the actual POP will always be either 0% or 100%,i.e. either there was or was not a measurable amount of precipitation.However, this eventual outcome is not known with certainty in advance,which is why POP forecasts are typically a percentage between 0% and100%. Although a POP forecast is the chance that measurableprecipitation will occur, it does not indicate how light or heavy theprecipitation might be. A POP-T forecast is a POP forecast where thetime period interval is T hours (i.e., POP-3 is a three-hour POPforecast, a POP-4 is a four-hour forecast, etc.).

The preferred embodiment of the present invention is a multi-periodPOP-3 forecast that includes a prediction of the most probable amount ofprecipitation that may fall during each time period. The POP-3 valuesare based on POP-6 values that have already been computed.

The resulting POP-3 estimates are presented in alphanumeric andgraphical means and communicated via a web page maintained on andgenerated by a server connected to the Internet. In the preferredembodiment, the presentation of forecast information is as follows. Foreach of selected time periods (e.g., the next three hours, the next sixhours, the next 12 hours, the next 24 hours, the next 48 hours and thenext 96 hours (i.e., four days)), a POP value, expressed in percentranging from 0% to 100%, is provided along with a graphic in the form ofa bar, the length of which conveys the probability value (i.e., thelonger the bar, the higher the POP value). Next to the POP value and baris a numerical value representing the most probable amount ofprecipitation that may fall during the time period, expressed inequivalent inches of water (i.e., rain, melted snow and ice combined).Information for the specific types of precipitation is also calculatedand may be communicated in the same manner. This includes probability ofprecipitation, as well as probability of snow, probability of ice,probability of rain, probability of thunderstorms and probability ofother events. Other information and indicia may also be presented invarious other forms.

In the preferred embodiment, each of the selected time periods startswith the current hour, so a high POP-3 value in the next few hours willcarry over into all the longer time periods. For example, if the POP-3in the next three hours time period is 90, then the POP in the next96-hour time period will be at least 90, even if there is no chance ofprecipitation after the first three hours of the period. To provide abetter understanding of this, a time-series graph depicting when theprecipitation will likely occur is also provided (by three-hourintervals). For each time period on the x-axis, a vertical bar is usedto represent the POP value. Thus, the height of the bars visuallyconveys when the precipitation event is forecast to occur. Each POP-3value is reset at the start of the next hour, so the forecast isprovided on a rolling basis.

In one embodiment of the invention, POP-3 forecast values are calculatedusing algorithm models that combine POP-6 forecasts for multiple periodsand the most probable forecast hourly precipitation amounts. In thosealgorithms, POP-6 values are compared, and based upon their relationshipto each other and the time period in question of less than 6 hours, avalue “A” is assigned. Next, the most probable precipitation amount foreach hour in the time period in question and in the hours surroundingthat time period are examined, and based upon their relationship, avalue of “B” is assigned. Then, a POP for the time period in question isdetermined based upon the relationship between the “A” and the “B”values. The “A” and the “B” values may be calculated utilizing theoutput from computerized numerical forecast models and may be modifiedbased upon current data from manual or sensor observations of currentconditions, as well as data from radar and satellite systems. The valuesmay also be calculated and modified manually.

Accordingly, it is a principal object of the present invention toprovide location-specific short-term POP forecasts for less thansix-hour time periods and preferably for three-hour time periods.

It is another object of the present invention to providelocation-specific short-term POP forecasts that are calculated for thecurrent time period and on a rolling time-period basis thereafter.

It is still another object of the present invention to provide forecaststo end users via a communications channel, including, but not limited towired, wireless and hardcopy channels such as a web page or e-mail onthe Internet, a pager, telephony (voice and/or data), broadcast, printedpage, and personal data organizer, each of which may include avoice-activated interface.

It is another object of the present invention to provide a system forforecasting and providing POP information to interested persons who maybe interested in monitoring POP forecasts via the Internet by loggingonto a server through a client station and entering user-specific inputssuch as location or region to obtain the desired information.

It is still another object of the present invention to provide mostprobable precipitation amount forecasts for each hour in the time periodin question.

It is another object of the present invention to provide probabilityforecasts for each different type of precipitation, including rain,snow, ice and thunderstorms.

It is still another object of the present invention to provide mostprobable precipitation amounts for each different type of precipitation.

These and other objects and features of the present invention areaccomplished as embodied and fully described herein by a systeminvolving a network server; a database populated with meteorologicaldata and forecast information contained on the network server or anothercomputer system operatively connected to the network server; a forecastmodel that executes algorithms (which input the meteorological data andforecast information from the database and output probability forecastinformation); and a communications system that generates information forend users, and is preferably a web-page generating software device, suchas a hypertext mark-up language (HTML) or other proprietary pagegenerator for generating a user.

Other objects, features and advantages of the present invention willbecome evident to one skilled in the art from the following detaileddescription of the invention in conjunction with the referenceddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of one embodiment of the system of thepresent invention;

FIG. 2a is a process flow diagram of the invention;

FIG. 2b is a continuation of the process flow diagram of FIG. 2a;

FIG. 2c is a continuation of the process flow diagram of FIG. 2b;

FIG. 2d is a continuation of the process flow diagram of FIG. 2c;

FIG. 2e is a continuation of the process flow diagram of FIG. 2d;

FIG. 2f is a continuation of the process flow diagram of FIG. 2e;

FIG. 2g is a continuation of the process flow diagram of FIG. 2f;

FIG. 2h is a continuation of the process flow diagram of FIG. 2g;

FIG. 2i is a continuation of the process flow diagram of FIG. 2h;

FIG. 2j is a continuation of the process flow diagram of FIG. 2i;

FIG. 2k is a continuation of the process flow diagram of FIG. 2j; and

FIG. 2l is a continuation of the process flow diagram of FIG. 2k.

DETAILED DESCRIPTION OF THE INVENTION

In the present invention, several preferred embodiments are describedfor illustrative purposes. Referring first to FIG. 1, which is aschematic diagram of the preferred embodiment of the system of thepresent invention, there is shown a computer-assisted probabilityforecast and report generator 105 that generates POP-T forecasts. In thepreferred embodiment of the invention, time period interval, T, is lessthan 6 hours. In the most preferred embodiment of the invention, T=3hours. The probability forecast information is provided for the currenttime through a pre-set time period, t=12, 24, 48 and/or 96 hours. Foreach time period, a POP is calculated for each successive time periodinterval (i.e., for T=3, values for POP-3⁽⁰⁻³⁾, POP-3⁽³⁻⁶⁾, POP-3⁽⁶⁻⁹⁾),. . . POP-3_((t−3 to t)) are calculated).

The forecast generator 105 preferably receives inputs from databaseserver 115, which contains a forecast database and which receives dataand information from various external sources. Those sources mayinclude, but are not limited to, current observational data source 110;historical and climatological database source 120; numerical modelforecast source 122; and direct input 125 from operational forecastmeteorologists.

The current observational data source 110 may be satellite, radar,automated and manual surface observation systems or any other systemsthat provide real-time or near real-time data and information. Forexample, NEXRAD radar data may be accessed and downloaded or telemetryfrom geosynchronous weather satellites may be collected and stored. Thehistorical and climatological database source 120 includes data forvarious meteorological parameters and it may or may not be connected toa network. That is, the data may be input to database server 115 from adigital source, such as a magnetic tape, or entered manually from hardcopy printouts. The numerical model forecast source 122 includescomputer-generated forecasts created by atmospheric forecast models runby U.S. and/or foreign governments, as well as those run by privateentities and universities, and it may or may not be connected to thenetwork.

The forecast generator 105 produces POP forecast information and makesit available through a network server 130, which is connected to anetwork like the Internet and may create a web page. An end user mayaccess the forecast information by logging onto the network server 130through a client computer 135 connected to the Internet, or may receivethe information via a pager or telephony system 140 or a broadcastsystem 145. Other channels of communication not expressly shown in FIG.1 are also contemplated, including, but not limited to wired, wirelessand hardcopy channels such as a web page or e-mail on the Internet, apager, telephony (voice and/or data), broadcast, printed page, andpersonal data organizer, each of which may include a voice-activatedinterface.

The forecast generator 105 computes the POP values and the most probableprecipitation amount values corresponding to each hour of the selectedtime period of interest, utilizing the artificial intelligence techniqueknown as an expert system, which is designed to simulate the thoughtprocesses and the procedure that might be followed by an expert indetermining these predictions, based upon the best available data. Inthis process, the expert might base the POP values on existing POP-6values and forecast hourly precipitation amounts, which those havingordinary skill in the art will be familiar with and know how toestimate.

In the case of the preferred embodiment of the invention, a POP-6 is asix-hour POP that already exists in a digital forecast database that mayreside in the same computer as the forecast generator 105 or it mayreside in the database server 115 that is connected to the forecastgenerator 105. The POP-6_(C) is the six-hour POP that contains at leastthe earliest part of POP-6 that already exists in the database (i.e.,the current POP-6). POP-6_((C+1)) is the six-hour POP from the databasefor the six-hour period that immediately follows POP-6_(C).POP-6_((C−1)) is the six-hour POP from the database that immediatelyprecedes the POP-6_(C).

Referring now to FIG. 2a, which is a process flow diagram of the presentinvention, the forecast generator 105 performs several basic steps asshown. First, in process step 205, a value for POP-T_(X) for aparticular time period is determined based upon the forecastprecipitation amount for each hour in the period. In process step 210, avalue for POP-T_(Y) for a particular time period is computed based onthe POPs for longer time periods using computations executed in module“A” or “B.” Next, in process step 220, POP-T_(C), the POP forecast valuefor this period, is set to the average of the POP-T_(X) and POP-T_(Y)values and then adjusted, as needed (as described later). Eachsuccessive POP-T value is also calculated (i.e., POP-T_((C+1)),POP-T_((C+2)), etc.). Then, based on the POP-T values for each interval,the six, 12, 24, 48 and 96 POP values are calculated in process step225. Finally, the results are communicated to an end user in processstep 230. This process can be used for precipitation or for anyindividual form of precipitation, such as rain, snow, ice orthunderstorms.

In the preferred embodiment of the invention, T=3 hours, so POP-3 valuesare computed. A POP-3 is the percentage chance that a measurable amountof precipitation (at least 0.01 inches of rain or ice or 0.1 inches ofsnow) will fall at a specific location during a three hour time period.In process step 205, an appropriate POP-3_(X) value is obtained from thefollowing table (where Tr=trace):

Number of hours in the time period with a trace of precipitationPOP-3_(X) 0 1 2 3 Number of hours 0 10 40 45 50 in the time period 1 5060 65 — where precipitation 2 70 80 — — is at least 0.01 inches 3 90 — ——

The following example explains the concept of using the above table toselect an appropriate POP-3_(X) value. If there are no hours in the nextthree hours that have an hourly precipitation forecast of a trace amountand there are two hours in the next three hours where the hourlyprecipitation forecast is at least 0.01 inches, then, reading downcolumn “0” to row “2” gives a value of 0.70 (i.e., 70%) for POP-3_(X).

Still referring to FIG. 2a, values for POP-3_(Y) are determined usingone of two modules “A” or “B” that execute depending upon the answer toquery step 215. Modules “A” and “B” are designed to simulate anintuitive analysis using artificial intelligence embodied in thealgorithms of those modules. The problem that must be addressed is thata POP-6 represents a value for a six-hour time period (hours 1-6) and weare interested in obtaining a three-hour time period sub-set withinthose six hours (or a three-hour time period that straddles two POP-6six hour time periods). For example, in its simplest terms, if POP-6 is60% for hours 0-6, what is the POP-3 for the three-hour interval forhours 0-3 and the three-hour interval for hours 3-6. The POP-3_(Y)values are determined by looking at the POP-6 values and the hourlyprecipitation amounts for the six-hour time period. If, for example,POP-6_((C−1)) is 90% and POP-6_((C+1)) is zero, then POP-3_(C) islogically somewhere between zero and 90%. If the precipitation forecastssuggest that measurable precipitation will occur during the first threehours of the POP-6_((C+1)) time period, the value for POP-3_(C) may becloser to the 90% value. The specific method for estimating thePOP-3_(Y) values from POP-6 data are illustrated below by reference tothe accompanying figures.

Still referring to FIG. 2a, query step 215 requires a determination ofwhether all the hours in POP-3 are in the same POP-6 time period. Theanswer to that query would be “No” if hour 1 of POP-3, for example,corresponds to hour 6 of POP-6_(C) but hours 2-3 of POP-3 correspond tohours 1 and 2, respectively, of POP-6_((C+1)). POP-6 and hourlyprecipitation forecast values for the location of interest are stored indatabase server 115 (FIG. 1) and are previously calculated by methodswell known in the art.

Referring now to FIG. 2b, which represents computation module “A,” querystep 235 requires a determination of whether the amount of precipitationfor each hour of POP-3 is equal to zero. Query step 240 requires adetermination of whether the amount of precipitation for each hour inPOP-3 is less than or equal to T. Query step 245 requires adetermination of whether there is any amount of precipitation for anyhour in POP-6_(C), other than the hours for the POP-3 time period, thatis greater than or equal to 0.01. Query step 250 requires adetermination of whether the amount of precipitation for all the hoursin POP-6_(C) is equal to zero. Query step 255 requires a determinationof whether the amount of precipitation for all the hours in POP-6_(C) isless than or equal to a trace amount. Query step 260 requires adetermination of whether the amount of precipitation for at least one ofthe two hours before and at least one of the two hours after POP-3 isgreater than or equal to 0.01 inches. Depending on the answers to thosequeries, as shown in FIG. 2b, different modules of the forecastgenerator 105 will be executed as discussed below.

In FIG. 2c, which represents module “B,” query step 265 requires adetermination of whether the amount of precipitation for all of thehours in POP-3 is equal to zero. Query step 270 requires a determinationof whether the amount of precipitation for all the hours in POP-3 isless than or equal to a trace amount. Query step 275 requires adetermination of whether the amount of precipitation for any hour inPOP-6_(C) or POP-6_((C+1)), that is also not an hour in POP-3, that isgreater than or equal to 0.01 inches. Query step 280 requires adetermination of whether the amount of precipitation for all the hoursin POP-6_(C) and POP-6_((C+1)) is equal to zero. Query step 285 requiresa determination of whether the amount of precipitation for all the hoursin POP-6_(C) and POP-6_((C+1)) is less than or equal to a trace amount.Query step 290 requires a determination of whether the amount ofprecipitation for any hour in both POP-6_(C) and POP-6_((C+1)), that isnot an hour in POP-3, that is greater than or equal to 0.01 inches.Depending on the answers to those queries, as shown in FIG. 2c,different modules of the forecast generator 105 will be executed asdiscussed below.

Next, in FIG. 2d, which represents module “C,” query step 295 requires adetermination of whether the amount of precipitation for POP-6_(C) isless than or equal to a trace amount. Query step 300 requires adetermination of whether the amount of precipitation for all of thehours in POP-6 that is greater than or equal to 0.01 inches is for thehours before the hours for POP-3. Query step 305 requires adetermination of whether the amount of precipitation for all the hoursin POP-6 that is greater than or equal to 0.01 inches is for the hoursafter the hours for POP-3. Depending on the answers to those queries, asshown in FIG. 2d, different modules of the forecast generator 105 willbe executed as discussed below.

Next, in FIG. 2e, which represents module “D,” query step 310 requires adetermination of whether the amount of precipitation for any of thethree hours before POP-3 is greater than or equal to 0.01 inches. Querystep 315 requires a determination of whether the amount of precipitationfor any of the three hours after POP-3 is greater than 0.01 inches.Depending on the answers to those queries, as shown in FIG. 2e,different modules of the forecast generator 105 will be executed asdiscussed below.

In FIG. 2f, which represents module “E,” query step 320 requires adetermination of whether the amount of precipitation for all the hoursin POP-6 that is greater than or equal to 0.01 inches are for hoursbefore the hours for POP-3. Query step 325 requires a determination ofwhether the amount of precipitation for all the hours in POP-6 that isgreater than or equal to 0.01 inches is for hours that are after thehours for POP-3. Depending on the answers to those queries, as shown inFIG. 2f, different modules of the forecast generator 105 will beexecuted as discussed below.

Next, in FIG. 2g, which represents module “F,” query step 330 requires adetermination of whether the amount of precipitation in any hours inPOP-6, other than the hours for POP-3, are equal to a trace amount.Query step 335 requires a determination of whether the amount ofprecipitation for all the hours in POP-6 that is equal to a trace amountare for hours before the hours for POP-3. Query step 340 requires adetermination of whether the amount of precipitation for all the hoursin POP-6 that is equal to a trace amount is for hours that are after thehours for POP-3. Depending on the answers to those queries, as shown inFIG. 2g, different modules of the forecast generator 105 will beexecuted as discussed below.

In FIG. 2h and FIG. 2i, which represents modules “L” and “M,”respectively, query step 345 requires a determination of whether theamount of precipitation in any of the last three hours of POP-6_((C−1))is greater than or equal to 0.01 inches. Query step 350 requires adetermination of whether the amount of precipitation for any of thefirst three hours of POP-6_((C+1)) is greater than or equal to 0.01inches. Depending on the answers to those queries, different modules ofthe forecast generator 105 will be executed as discussed below.

Next, in FIG. 2j, which represents module “Q,” query step 355 requires adetermination of whether the amount of precipitation for all the hoursin POP-6_(C) is equal to zero. Query step 360 requires a determinationof whether the amount of precipitation for all the hours inPOP-6_((C+1)) is equal to zero. Depending on the answers to thosequeries, different modules of the forecast generator 105 will beexecuted as discussed below.

In FIG. 2k, which represents module “S,” query step 365 requires adetermination of whether the amount of precipitation for all the hoursin POP-6_(C) is less than or equal to a trace amount. Query step 370requires a determination of whether the amount of precipitation for allof the hours in POP-6_((C+1)) is less than or equal to a trace amount.Depending on the answers to those queries, different modules of theforecast generator 105 will be executed as discussed below.

Referring to FIGS. 2a-2k, the following table lists the values assignedto the variable POP-3_(Y) after executing one or more of the previousmodules.

Module POP-3_(Y) G 0.9 * POP-6_(C) H 0.8 * POP-6_(C) I (0.5 *POP-6_(C)) + (0.4 * POP-6_((C-1))) J (0.5 * POP-6_(C)) + (0.4 *POP-6_((C+1))) K 0.7 * POP-6_(C) N (0.6 * POP-6_(C)) + (0.2 *POP-6_((C-1))) O (0.6 * POP-6_(C)) + (0.2 * POP-6_((C+1))) W 0.9 * max(POP-6_(C), POP-6_((C+1))) V 0.8 * max (POP-6_(C), POP-6_((C+1))) U max(0.45 * (POP-6_(C) + POP-6_((C+1))), 0.8 * max(POP-6_(C) +POP-6_((C+1)))) R (0.4 * POP-6_(C)) + (0.4 * POP-6_((C+1))) T (0.3 *POP-6_(C)) + (0.3 * POP-6_((C+1)))

As shown in the table above, the generalized formula for POP-3_(Y) is asfollows, for modules G, H, J, K, 0, R and T:POP-3_(Y)=a*(POP-6_(C))+b*(POP-6_((C+1)))  (I)Where: 0≤a≤1 and 0≤b≤1. For modules I and N, the generalized formula forPOP-3_(Y) is as follows:POP-3_(Y)=a*(POP-6_(C))+b*(POP-6_((C−1)))  (II)Where: 0≤a≤1 and 0≤b≤1. For modules W and V, the generalized formula forPOP-3_(Y) is as follows:POP-3_(Y)=a*max(POP-6_(C),POP-6_((C−1)))  (III)Where: 0≤a≤1.

Once POP-3_(Y) is known, the forecast generator 105 executes the routineshown in FIG. 21 (i.e., process step 220 shown in FIG. 2a). In processstep 375, POP-3_(C) is calculated using the following relationship:POP-3_(C)=½(POP-3_(X)+POP-3_(Y))Where POP-3_(C) is the three-hour POP for the current time period (i.e.,hours 0-3) beginning with the current hour. Values for POP-3_(C) areadjusted, as necessary, in order to make the resulting POPs consistentwith the definition of POP. If, for example, the calculated value forPOP-3_(Y) using one of the modules from the table above is 30% but thehourly precipitation forecast data indicates there will be precipitationin one of the three hours of the time period, the probability ofprecipitation must be at least 50% for the POP time period.

Query step 380 requires a determination of whether the amount ofprecipitation for any of the hours in POP-3_(C) are greater than orequal to a trace amount. Query step 385 requires a determination ofwhether POP-3_(C) is less than or equal to 40. Query step 400 requires adetermination of whether the amount of precipitation for any hours inPOP-3_(C) is greater than or equal to 0.01 inches. Query step 405requires a determination of whether POP-3_(C) is less than or equal to50. Query step 415 requires a determination of whether POP-3_(C) isgreater than or equal to 50.

Depending upon the answers to the above queries, process step 390assigns a value of 40 to POP-3_(C), process step 395 assigns the currentvalue of POP-3_(C) to the variable POP-3_(C) and process step 410assigns a value of 50 to POP-3_(C). Thus, the value for POP-3_(C) isequal to 40, 50 or the current calculated value for POP-3_(C).

Referring back to FIG. 2a, when the routine is finished for the firsttime period T_(C), the calculations are repeated for each subsequentconsecutive time periods. Thus, there will be 32 values for POP-3_(X)and POP-3_(Y) and POP-3_(C) for a 96-hour period, which leads to 32values for POP-3, one for each of the 32 three-hour periods. In processstep 225, POP values for each interval of interest are determined (i.e.,POP-6, POP-12, POP-24, POP-48 and POP-96, or others). To do this, eachconsecutive pairs of POP values are combined (e.g., hours 0-3 and 3-6),which reduces the number of POP values from 32 to 16. Those valuesrepresent POP-6. Next, each consecutive pairs of POP-6 values arecombined (e.g., hours 0-6 and 6-12.), which reduces the number of POPvalues to 8. This process is repeated to come up with a single POP-96value. Combining two successive probability values is done according tothe following formula:If POP-T_(C) is lower than POP-T_((C+1)), thenPOP-(2T)=1−(1−0.4*POP-T_(C))*(1−POP-T_((C+)))Otherwise, POP-(2T)=1−(1−POP-T_(C))*(1−0.4*POP-T_((C+1)))For example, where T=3 hours (so 2T=6 hours) and given values forPOP-3_(C)=40 (i.e., hours 1-3) and POP-3_((C+1))=60 (i.e., hours 4-6):POP-6=1−(1−0.4*0.4)*(1−0.6)POP-6=0.664(i.e., 66%)

In the preferred embodiment, once the values for probability ofprecipitation and most probable precipitation amount have been computedfor each three-hour time period, the values are made available to endusers via the network server 130. That server generates and sends webpage content through a network (i.e., the Internet) to a client computerwhere it is displayed on the client computer users' monitor as a webpage. The information is preferably shown numerically and graphicallyfor each of six time periods: T through T+3 (i.e., the next three hourtime period); T through T+6 (i.e., the next six hour time period); Tthrough T+12 (12 hour time period); T through T+24 (one day timeperiod); T through T+48 (two day time period) and T through T+96 (fourday time period). It is preferably also shown for each consecutivethree-hour period from hour 0 through hour 96. Methods for generatingweb page content and displaying data numerically and graphically arewell known in the art, as are other methods for communicating the asidentified herein.

Although this invention has been described in connection with specificembodiments, objects and purposes for the invention, it will beappreciated by one of skill in the art that various modifications of theinvention, other than those discussed above, may be resorted to withoutdeparting from the nature and scope of the invention.

What is claimed is:
 1. A system for calculating and communicatingprobability of precipitation forecasts for periods less than six hoursusing existing forecasting information, the system comprising: storagemeans for storing location-specific probability forecasting information;a precipitation forecasting system including processing means forcomputing a probability of precipitation value from the forecastinformation for any time period interval, T, in a pre-set time period,t, wherein the precipitation forecasting system uses artificialintelligence to compute probability of precipitation values for timeperiod intervals of length T in pre-set time periods of length t basedon: a number of time periods within the time period interval of length Twherein a forecasted precipitation amount is a trace amount; a number oftime periods within the time period interval of length T wherein aforecasted precipitation amount is greater than or equal to a pre-setamount; a probability of precipitation value for the pre-set time periodof length t; a probability of precipitation value for a next consecutivetime period of length t; and a probability of precipitation value for aprevious consecutive time period of length t; and a communicationssubsystem for communicating the probability of precipitation value viaat least one communications channel, wherein T<6 hours and t≥6 hours. 2.The system according to claim 1, wherein T=3 hours and t=96 hours. 3.The system according to claim 1, wherein the probability ofprecipitation value are is not fixed to a specific time, but are isrecalculated to a present time.
 4. The system according to claim 1,further comprising manipulation means for allowing a system operator tomanually adjust the probability of precipitation value and the mostprobable precipitation amount value.
 5. The system according to claim 1,wherein the storage means is a networked computer containing a digitaldatabase containing the location-specific probability forecastinginformation.
 6. The system according to claim 1, wherein the processingmeans comprises a computer executing a probability forecast model. 7.The system according to claim 1, wherein the communications subsystem isa computer server connected to a network, and wherein the at least onecommunications channel comprises one or more web pages having theprobability of precipitation value and the most probable precipitationamount value upon receiving a request from a remote client connected tothe network.
 8. The system according to claim 1, wherein the probabilityof precipitation forecast is the a current three-hour probabilityforecast value determined from:½*(POP-3_(X)+POP-3_(Y)), where POP-3_(X) is a value from between 10 and90, inclusively, and POP-3_(Y) is determined from the formula:POP-3_(Y)=a*(POP-6_(C))+b*(POP-6_((C+1))) where: 0≤a≤1 and 0≤b≤1 andPOP-6_(C) is the a six-hour probability of precipitation forecast valuefor the a current six-hour time interval already stored in the storagemeans and POP-6_((C+1)) is the a next consecutive six-hour time intervalalso already stored in the storage means.
 9. The system according toclaim 1, wherein the probability of precipitation forecast is the acurrent three-hour probability forecast value determined from:½*(POP-3_(X)+POP-3_(Y)), where POP-3_(X) is a value from between 10 and90, inclusively, and POP-3_(Y) is determined from the formula:POP-3_(Y)=a*(POP-6_(C))+b*(POP-6_((C−1))) where: 0≤a≤1 and 0≤b≤1 andPOP-6_(C) is the a six-hour probability of precipitation forecast valuefor the a current six-hour time interval already stored in the storagemeans and POP-6_((C−1)) is the a previous consecutive six-hour timeinterval also already stored in the storage means.
 10. The systemaccording to claim 1, wherein the probability of precipitation forecastis the a current three-hour probability forecast value determined from:½*(POP-3_(X)+POP-3_(Y)), where POP-3_(X) is a value from between 10 and90, inclusively, and POP-3_(Y) is determined from the formula:POP-3_(Y)=a*max(POP-6_(C),POP-6_((C−1))) where: 0≤a≤1 and POP-6_(C) isthe a six-hour probability of precipitation forecast value for the acurrent six-hour time interval already stored in the storage means andPOP-6_((C−1)) is the a previous consecutive six-hour time interval alsoalready stored in the storage means.
 11. The system according to claim1, wherein in addition to the probability of precipitation forecast,also calculated are probability forecasts for specific types ofprecipitation, including but not limited to, the selected from one of aprobability of rain, the a probability of snow, the a probability ofice, and the a probability of thunderstorms.
 12. The system according toclaim 1, wherein most probable precipitation amount values arecalculated for some or all of the time period intervals T.
 13. A systemfor calculating and communicating probability of precipitation forecastsfor periods that are not fixed to specific pre-set times using existingforecasting information, the system comprising: storage means forstoring location-specific probability forecasting information; aprecipitation forecasting system including processing means forcomputing a probability of precipitation value from the forecastinformation for any time period interval, T, in a pre-set time period,t, wherein the precipitation forecasting system uses artificialintelligence to compute probability of precipitation values for timeperiod intervals of length T in the pre-set time periods of length tbased on: a number of time periods within the time period interval oflength T wherein a forecasted precipitation amount is a trace amount; anumber of time periods within the time period interval of length Twherein a forecasted precipitation amount is greater than or equal to apre-set amount; a probability of precipitation value for the pre-settime period of length t; a probability of precipitation value for a nextconsecutive time period of length t; and a probability of precipitationvalue for a previous consecutive time period of length t; and acommunications subsystem for communicating the probability ofprecipitation value via at least one communications channel, wherein theprobability of precipitation value are is not fixed to a specificpre-set time, but are is recalculated to a present time.
 14. The systemaccording to claim 13, wherein T<6 hours and t≥6 hours.
 15. The systemaccording to claim 13, wherein T=3 hours and t=96 hours.
 16. The systemaccording to claim 13, further comprising manipulation means forallowing a system operator to manually adjust the probability ofprecipitation value and the most probable precipitation amount value.17. The system according to claim 13, wherein the processing meanscomprises a computer executing a probability forecast model.
 18. Thesystem according to claim 13, wherein the communications subsystem is acomputer server connected to a network, and wherein the at least onecommunications channel comprises one or more web pages having theprobability of precipitation value and the most probable precipitationamount value upon receiving a request from a remote client connected tothe network.
 19. The system according to claim 13, wherein theprobability of precipitation forecast is the a current three-hourprobability forecast value determined from:½*(POP-3_(X)+POP-3_(Y)), where POP-3_(X) is a value from between 10 and90, inclusively, and POP-3_(Y) is determined from the formula:POP-3_(Y)=a*(POP-6_(C))+b*(POP-6_((C+1))) where: 0≤a≤1 and 0≤b≤1 andPOP-6_(C) is the a six-hour probability of precipitation forecast valuefor the current six-hour time interval already stored in the storagemeans and POP-6_((C+1)) is the a next consecutive six-hour time intervalalso already stored in the storage means.
 20. The system according toclaim 13, wherein the probability of precipitation forecast is the acurrent three-hour probability forecast value determined from:½*(POP-3_(X)+POP-3_(Y)), where POP-3_(X) is a value from between 10 and90, inclusively, and POP-3_(Y) is determined from the formula:POP-3_(Y)=a*(POP-6_(C))+b*(POP-6_((C−1))) where: 0≤a≤1 and 0≤b≤1 andPOP-6_(C) is the a six-hour probability of precipitation forecast valuefor the current six-hour time interval already stored in the storagemeans and POP-6_((C−1)) is the a previous consecutive six-hour timeinterval also already stored in the storage means.
 21. The systemaccording to claim 13, wherein the probability of precipitation forecastis the a current three-hour probability forecast value determined from:½*(POP-3_(X)+POP-3_(Y)), where POP-3_(X) is a value from between 10 and90, inclusively, and POP-3_(Y) is determined from the formula:POP-3_(Y)=a*max(POP-6_(C),POP-6_((C−1))) where: 0≤a≤1 and POP-6_(C) isthe six-hour probability of precipitation forecast value for the currentsix-hour time interval already stored in the storage means andPOP-6_((C−1)) is the previous consecutive six-hour time interval alsoalready stored in the storage means.
 22. The system according to claim13, wherein in addition to the probability of precipitation forecast,also calculated are probability forecasts for specific types ofprecipitation, including but not limited to, the selected from one of aprobability of rain, the a probability of snow, the a probability ofice, and the a probability of thunderstorms.
 23. The system according toclaim 13, wherein most probable precipitation amount values arecalculated for some or all of the time period intervals T.
 24. A systemfor calculating and communicating probability of precipitation forecastsusing existing forecasting information, the system comprising: aprecipitation forecasting system including: a probability ofprecipitation forecast model for computing a probability ofprecipitation value for a time interval, T, within a pre-set timeperiod, t, wherein T<6 hours and t≥6 hours, and wherein the probabilityof precipitation model uses artificial intelligence to computeprobability of precipitation values for the time intervals of length Tin pre-set time periods of length t based on: a number of time periodswithin the time interval of length T wherein a forecasted precipitationamount is a trace amount; a number of time periods within the timeinterval of length T wherein a forecasted precipitation amount isgreater than or equal to a pre-set amount; a probability ofprecipitation value for the pre-set time period of length t; aprobability of precipitation value for a next consecutive time period oflength t; and a probability of precipitation value for a previousconsecutive time period of length t; a most probable precipitationamount forecast model for computing a precipitation amount valuecorresponding to each probability of precipitation value; and acommunications device for communicating the probability of precipitationand precipitation amount values electronically to a remote requester.25. The system according to claim 24, wherein T=3 hours and t=96 hours.26. The system according to claim 24, wherein the probability ofprecipitation value are is not fixed to a specific time, but are isrecalculated to the present time.
 27. The system according to claim 24,wherein in addition to the probability of precipitation forecast, alsocalculated are probability forecasts for specific types ofprecipitation, including but not limited to, the selected from one of aprobability of rain, the a probability of snow, the a probability ofice, and the a probability of thunderstorms.
 28. The system according toclaim 24, wherein the communications device is a network serverconnected to the Internet having a web page generator for sending webcontent in response to a request from a client computer connected to theInternet.
 29. The system according to claim 24, wherein thecommunications device is one of a wired or wireless telephony system, apager, radio or television broadcast system and a hardcopy printout. 30.A computer-implemented method of calculating probability ofprecipitation and most probable amount of precipitation forecasts forselected time periods and locations and communicating the same to endusers, comprising the steps of, at a precipitation forecasting system:(a) storing probability of precipitation values from meteorologicalforecast models; (b) calculating, by the precipitation forecastingsystem, a location-specific probability of precipitation value for eachconsecutive time period intervals, T, contained within the pre-set timeperiod, t, wherein the precipitation forecasting system uses artificialintelligence to compute probability of precipitation values for timeperiod intervals of length T in pre-set time periods of length t basedon: a number of time periods within the time period interval of length Twherein the most probable amount of precipitation is a trace amount; anumber of time periods within the time period interval of length Twherein the most probable amount of precipitation is greater than orequal to a pre-set amount; a probability of precipitation value for thepre-set time period of length t; a probability of precipitation valuefor a next consecutive time period of length t; and a probability ofprecipitation value for a previous consecutive time period of length t;(c) calculating, by the precipitation forecasting system, a mostprobable precipitation amount corresponding to each of the probabilityof precipitation values; (d) calculating, by the precipitationforecasting system, a location-specific probability of precipitationvalue for each t/T pairs of consecutive probability of precipitationvalues; and (e) communicating via at least one communications channelsaid location-specific probability of precipitation values for eachconsecutive time period intervals, T, said most probable precipitationamount, and said probability of precipitation value for each t/T pairs,to an end user.
 31. The system according to claim 13, wherein thestorage means is a networked computer containing a digital databasecontaining the location-specific probability forecasting information.32. The system according to claim 13, wherein the location-specificprobability forecasting information includes information about aspecific location.
 33. The system according to claim 1, furthercomprising an input module for manually entering an adjusted probabilityof precipitation value.
 34. The system according to claim 13, furthercomprising an input module for manually entering an adjusted probabilityof precipitation value.
 35. The system according to claim 1, wherein thetime period, T, is selected from one of 1, 2, 3, 4, 5, 6, 10, 12, 15,30, 45, 60, 120, 180, 240, and 300 minutes.
 36. The system according toclaim 1, wherein the pre-set time period, t, is selected from one of 6,7, 8, 9, 10, 12, 24, 36, 48, and 96 hours.
 37. The system according toclaim 13, wherein the time period, T, is selected from one of 1, 2, 3,4, 5, 6, 10, 12, 15, 30, 45, 60, 120, 180, 240, and 300 minutes.
 38. Thesystem according to claim 13, wherein the pre-set time period, t, isgreater than or equal to T and is selected from one of 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 12, 24, 36, 48, and 96 hours.
 39. The system according toclaim 24, wherein the time period, T, is selected from one of 1, 2, 3,4, 5, 6, 10, 12, 15, 30, 45, 60, 120, 180, 240, and 300 minutes.
 40. Asystem for calculating and communicating probability of precipitationforecasts, the system comprising: a storage device for storinglocation-specific probability forecasting information; a processingdevice for computing a probability of precipitation value from theforecast information for any pre-determined time period, T, in a pre-settime period, t, that is greater than or equal to the pre-determined timeperiod, wherein the processing device uses artificial intelligence tocompute probability of precipitation values for pre-determined timeperiods of length T in pre-set time periods of length t based on: anumber of time periods within the time pre-determined period of length Twherein a forecasted precipitation amount is a trace amount; a number oftime periods within the pre-determined time period of length T wherein aforecasted precipitation amount is greater than or equal to a pre-setamount; a probability of precipitation value for the pre-set time periodof length t; a probability of precipitation value for a next consecutivetime period of length t; and a probability of precipitation value for aprevious consecutive time period of length t; and a communicationssubsystem for communicating the probability of precipitation value viaat least one communications channel.
 41. A system for calculating andcommunicating probability of precipitation forecasts for periods thatare not fixed to specific pre-set times, the system comprising: astorage device for storing location-specific probability forecastinginformation; a processing device for computing a probability ofprecipitation value from the forecast information for any time period,T, in a pre-set time period, t, wherein the probability of precipitationvalues are not fixed to a specific pre-set time, but are recalculated toa present time, and wherein the processing device uses artificialintelligence to compute probability of precipitation values for timeperiods of length T in pre-set time periods of length t based on: anumber of time periods within the time period of length T wherein aforecasted precipitation amount is a trace amount; a number of timeperiods within the time period of length T wherein a forecastedprecipitation amount is greater than or equal to a pre-set amount; aprobability of precipitation value for the pre-set time period of lengtht; a probability of precipitation value for a next consecutive timeperiod of length t; and a probability of precipitation value for aprevious consecutive time period of length t; and a communicationssubsystem for communicating the probability of precipitation value viaat least one communications channel.
 42. A system for calculating andcommunicating probability of precipitation forecasts, the systemcomprising: a probability of precipitation system including: aprobability of precipitation forecast model for computing a probabilityof precipitation value for a time period, T, within a pre-set timeperiod, t, that is greater than or equal to the time period, wherein theprobability of precipitation forecast model uses artificial intelligenceto compute probability of precipitation values for time periods oflength T in pre-set time periods of length t based on: a number of timeperiods within the time period of length T wherein a forecastedprecipitation amount is a trace amount; a number of time periods withinthe time period of length T wherein a forecasted precipitation amount isgreater than or equal to a pre-set amount; a probability ofprecipitation value for the pre-set time period of length t; aprobability of precipitation value for a next consecutive time period oflength t; and a probability of precipitation value for a previousconsecutive time period of length t; a most probable precipitationamount forecast model for computing a precipitation amount valuecorresponding to each probability of precipitation value; and acommunications device for communicating the probability of precipitationand precipitation amount values electronically to a remote requestor.43. A computer-implemented method for calculating and communicating atleast one probability of precipitation forecast, comprising the stepsof, at a precipitation forecasting system: determining by theprecipitation forecasting system, a first probability of precipitationvalue for a first time period, T, within a pre-set time period, t, basedupon: a number of time periods within the first time period, T, whereina forecasted precipitation amount is a trace amount; and a number oftime periods within the first time period, T, wherein a forecastedprecipitation amount is greater than or equal to a pre-set amount;determining, by the precipitation forecasting system, a secondprobability of precipitation value for a second time period overlappingat least a part of the first time period, based upon: a probability ofprecipitation value for the pre-set time period of length t; aprobability of precipitation value for a next consecutive time period oflength t; or a probability of precipitation value for a previousconsecutive time period of length t; determining, by the precipitationforecasting system, a third probability of precipitation value based onthe first and second values; and communicating the third probability ofprecipitation value via at least one communications channel.
 44. Themethod according to claim 43, further comprising the step of adjustingthe third probability of precipitation value before performing the stepof communicating the value.
 45. The method according to claim 43,further comprising the step of determining a fourth probability ofprecipitation value for a third time period that is equal in duration tothe first time period, wherein the first and third time periods arewithin the pre-set time period, t.
 46. The method according to claim 45,further comprising the step of determining a fifth probability ofprecipitation value based on the third and fourth probability ofprecipitation values.
 47. The method according to claim 43, wherein thethird probability of precipitation value is a probability ofprecipitation of rain, snow, or ice.
 48. The method according to claim43, wherein the third probability of precipitation value is communicatedas a percentage chance.
 49. The method according to claim 43, furthercomprising communicating a most probable amount value for the first timeperiod via the least one communications channel.
 50. The methodaccording to claim 49, wherein the most probable amount value is anumerical value representing the most probable amount of precipitationthat may fall during the first time period, expressed in equivalentinches of water.
 51. The method according to claim 43, wherein the stepof communicating the third probability of precipitation value includes agraphic depicting when during the pre-set time period precipitation willlikely occur.
 52. The method according to claim 43, wherein thecommunications channel is the Internet.
 53. The method according toclaim 43, wherein the first time period is selected from one of 1, 2, 3,4, 5, 6, 10, 12, 15, 30, 45, 60, 120, 180, 240 and 300 minutes.
 54. Themethod according to claim 43, wherein the pre-set time period isselected from one of 1, 2, 3, 4, 5, 6, 7, 8, 10, 24, 36, 48, and 96hours.
 55. The method according to claim 43, wherein there are aplurality of time intervals before and after the first time period, anda plurality of equal time intervals within the first time period,further comprising the steps of: determining whether the amount ofprecipitation for each of the equal time intervals within the first timeperiod is equal to zero; if it is not determined that the amount ofprecipitation for each of the equal time intervals within the first timeperiod is equal to zero, determining whether the amount of precipitationfor each of the equal time intervals within the first time period isless than or equal to a trace amount; and determining whether the amountof precipitation for at least one time interval before the first timeperiod and the amount of precipitation for at least one time intervalafter the first time period is greater than or equal to 0.01 inches. 56.The method according to claim 43, further comprising the step of:determining for each of a plurality of equal time intervals within thefirst time period, T, whether the amount of precipitation for each ofthe plurality of equal time intervals is less than or equal to a traceamount.
 57. A system for calculating and communicating probability ofprecipitation forecasts, the system comprising: a storage device forstoring location-specific probability forecasting information; aprecipitation forecasting system including a processing device forcomputing a probability of precipitation value from the forecastinformation for each of a plurality of pre-determined time periods, T,in a pre-set time period, t, wherein the pre-set time period is greaterthan or equal to the plurality of pre-determined time periods, andwherein the probability of precipitation forecasting system usesartificial intelligence to compute probability of precipitation valuesfor pre-determined time periods of length T in pre-set time periods oflength t based on: a number of time periods within the pre-determinedtime period of length T wherein a forecasted precipitation amount is atrace amount; a number of time periods within the pre-determined timeperiod of length T wherein a forecasted precipitation amount is greaterthan or equal to a pre-set amount; a probability of precipitation valuefor the pre-set time period of length t; a probability of precipitationvalue for a next consecutive time period of length t; and a probabilityof precipitation value for a previous consecutive time period of lengtht; and a communications subsystem for communicating the probability ofprecipitation values via at least one communications channel.
 58. Asystem for calculating and communicating probability of precipitationforecasts for periods that are not fixed to specific pre-set times, thesystem comprising: a storage device for storing location-specificprobability forecasting information; a precipitation forecasting systemincluding a processing device for computing a probability ofprecipitation value from the forecast information for each of aplurality of time periods, T, in a pre-set time period, t, wherein theprobability of precipitation values are not fixed to a specific pre-settime, but are recalculated to a present time, and wherein theprecipitation forecasting system uses artificial intelligence to computeprobability of precipitation values for time periods of length T inpre-set time periods of length t based on: a number of time periodswithin the time period of length T wherein a forecasted precipitationamount is a trace amount; a number of time periods within the timeperiod of length T wherein a forecasted precipitation amount is greaterthan or equal to a pre-set amount; a probability of precipitation valuefor the pre-set time period of length t; a probability of precipitationvalue for a next consecutive time period of length t; and a probabilityof precipitation value for a previous consecutive time period of lengtht; and a communications subsystem for communicating the probability ofprecipitation values via at least one communications channel.
 59. Asystem for calculating and communicating probability of precipitationforecasts, the system comprising: a precipitation forecasting systemincluding: a probability of precipitation forecast model for computing aprobability of precipitation value for each of a plurality of timeperiods, T, in a pre-set time period, t, wherein the pre-set time periodis greater than or equal to the plurality of time periods, and whereinthe precipitation forecast model uses artificial intelligence to computeprobability of precipitation values for time periods of length T inpre-set time periods of length t based on: a number of time periodswithin the time period of length T wherein a forecasted precipitationamount is a trace amount; a number of time periods within the timeperiod of length T wherein a forecasted precipitation amount is greaterthan or equal to a pre-set amount; a probability of precipitation valuefor the pre-set time period of length t; a probability of precipitationvalue for a next consecutive time period of length t; and a probabilityof precipitation value for a previous consecutive time period of lengtht; a most probable precipitation amount forecast model for computing aprecipitation amount value corresponding to each of the probability ofprecipitation values; and a communications device for communicating eachof the probability of precipitation and most probable precipitationamount values electronically to a remote requestor.